The Indian Council of Medical Research (ICMR) has recently released the first-ever set of ethical guidelines for the application of artificial intelligence (AI) in biomedical research and healthcare.

Ethical Guidelines for AI usage in Healthcare

• The guidelines aim to create “an ethics framework which can assist in the development, deployment, and adoption of AI-based solutions” in specific fields.
• Through this initiative, the ICMR aims to make “AI-assisted platforms available for the benefit of the largest section of common people with safety and highest precision possible”.
• It seeks to address emerging ethical challenges when it comes to AI in biomedical research and healthcare delivery.

Key features

• Effective and safe development, deployment, and adoption of AI-based technologies: The guidelines provide an ethical framework that can assist in the development, deployment, and adoption of AI-based solutions in healthcare and biomedical research.
• Accountability in case of errors: As AI technologies are further developed and applied in clinical decision making, the guidelines call for processes that discuss accountability in case of errors for safeguarding and protection.
• Patient-centric ethical principles: The guidelines outline 10 key patient-centric ethical principles for AI application in the health sector, including accountability and liability, autonomy, data privacy, collaboration, risk minimisation and safety, accessibility and equity, optimisation of data quality, non-discrimination and fairness, validity and trustworthiness.
• Human oversight: The autonomy principle ensures human oversight of the functioning and performance of the AI system.
• Consent and informed decision making: The guidelines call for the attainment of consent of the patient who must also be informed of the physical, psychological and social risks involved before initiating any process.
• Safety and risk minimisation: The safety and risk minimisation principle is aimed at preventing “unintended or deliberate misuse”, anonymised data delinked from global technology to avoid cyber attacks, and a favourable benefit-risk assessment by an ethical committee among a host of other areas.
• Accessibility, equity and inclusiveness: The guidelines acknowledge that the deployment of AI technology assumes widespread availability of appropriate infrastructure and thus aims to bridge the digital divide.
• Relevant stakeholder involvement: The guidelines outline a brief for relevant stakeholders including researchers, clinicians/hospitals/public health system, patients, ethics committee, government regulators, and the industry.
• Standard practices: The guidelines call for each step of the development process to follow standard practices to make the AI-based solutions technically sound, ethically justified, and applicable to a large number of individuals with equity and fairness.
• Ethical review process: The ethical review process for AI in health comes under the domain of the ethics committee which assesses several factors including data source, quality, safety, anonymization, and/or data piracy, data selection biases, participant protection, payment of compensation, possibility of stigmatisation among others.

Policy moves for streamlining AI in Healthcare

• India already offers streamlining of AI technologies in various sectors, including healthcare, through the National Health Policy (2017), National Digital Health Blueprint (NDHB 2019), and Digital Information Security in Healthcare Act (2018) proposed by the Health Ministry.
• These initiatives pave the way for the establishment of the National Data Health Authority and other health information exchanges.

Potential applications of AI in healthcare

Artificial Intelligence (AI) has revolutionized the healthcare industry by enabling various applications. These applications include:

• Diagnosis and screening: AI can be used to identify diseases from medical images like X-rays, CT scans, and MRIs.
• Therapeutics: AI can assist in the development of personalised medicines by analyzing a patient’s genetic makeup.
• Preventive treatments: AI can predict the risk of developing a disease, helping healthcare professionals to take preventive measures.
• Clinical decision-making: AI can analyze large amounts of data to assist healthcare professionals in making treatment decisions.
• Public health surveillance: AI can be used to monitor disease outbreaks and inform public health policies.
• Complex data analysis: AI can analyze large amounts of data from multiple sources to identify patterns and inform healthcare decision-making.
• Predicting disease outcomes: AI can predict disease outcomes based on patient data, enabling early
• Behavioural and mental healthcare: AI can help diagnose and treat mental health conditions.
• Health management systems: AI can assist in managing patient records, appointment scheduling and reminders, and medication management.

Various challenges for imbibing

• Data privacy and security: With the use of AI in healthcare, there is a significant amount of personal and sensitive data is collected. This data needs to be kept secure and protected from potential cyber-attacks.
• Regulatory and ethical issues: AI technology is still in its early stages of development and there are no clear guidelines or regulations in place for its use in healthcare. There are also ethical considerations, such as accountability, transparency, and bias that need to be addressed.
• High cost involved: The implementation of AI in healthcare requires significant investment in terms of infrastructure, software, and training. This cost can be a major challenge for healthcare organizations, especially in developing countries.
• Integration with existing systems: AI systems need to be integrated with existing healthcare systems and processes. This can be challenging, especially in cases where the existing systems are outdated or incompatible with AI technology.
• Lack of trust and acceptance: AI technology is still relatively new in healthcare and there is a lack of trust and acceptance among healthcare professionals and patients. This can be a major hurdle in the widespread adoption of AI in healthcare.

Threats posed by AI to healthcare

• Data privacy and security: The use of AI in healthcare requires the collection and analysis of vast amounts of personal health data, which could be at risk of being stolen or misused.
• Bias and discrimination: There is a risk that AI algorithms could perpetuate existing biases and inequalities in healthcare, such as racial or gender bias.
• Lack of transparency: Some AI models are complex and difficult to understand, which can make it difficult to explain the reasoning behind a particular decision.
• Medical errors: AI systems can make errors if they are trained on biased or incomplete data, or if they are used inappropriately.
• Ethical concerns: There are several ethical concerns associated with the use of AI in healthcare, including the potential for AI to replace human doctors, the impact on patient autonomy, and the implications for informed consent.

Way forward

• Develop a national AI strategy for healthcare: This strategy should include policies for data sharing, privacy, and security, as well as guidelines for the ethical and responsible use of AI.
• Invest in AI research and development: The government should invest in research and development of AI technologies that can help address the challenges in healthcare.
• Promote collaboration between stakeholders: Collaboration between stakeholders such as healthcare providers, researchers, government agencies, and industry can help accelerate the development and adoption of AI technologies in healthcare.
• Train healthcare professionals in AI: The government can work with academic institutions and the industry to create training programs and certifications for healthcare professionals.
• Address regulatory challenges: The government should work to address regulatory challenges related to the use of AI in healthcare.
• Focus on affordability and accessibility: This can be achieved by promoting innovation, encouraging competition, and ensuring that AI technologies are integrated into existing healthcare infrastructure.

Get an IAS/IPS ranker as your 1: 1 personal mentor for UPSC 2024

Attend Now